Clipmivo Video
BarneyD66/clipmivo-tools
Create and manage AI video tasks through ClipmivoAI using its MCP server, CLI or REST API.
为分镜或自包含视频单元生成视频。当用户要求生成或重做视频时使用;支持整集、单项与批量自选. An agent skill from ArcReel/ArcReel.
The automated check flagged lines worth reading first. See the safety section below.
$ npx skills add ArcReel/ArcReel --skill generate-video -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ArcReel/ArcReel generate-video --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-video .claude/skills/generate-video && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "generate-video" agent skill from https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-video into .claude/skills/generate-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generate-video", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-videoType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ArcReel/ArcReel --skill generate-video -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ArcReel/ArcReel generate-video --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-video .agents/skills/generate-video && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "generate-video" agent skill from https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-video into .agents/skills/generate-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generate-video", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArcReel/ArcReel --skill generate-video -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ArcReel/ArcReel generate-video --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-video .cursor/skills/generate-video && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "generate-video" agent skill from https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-video into .cursor/skills/generate-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generate-video", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ArcReel/ArcReel.git --path agent_runtime_profile/.claude/skills/generate-video--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ArcReel/ArcReel --skill generate-video -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ArcReel/ArcReel generate-video --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-video .gemini/skills/generate-video && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "generate-video" agent skill from https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-video into .gemini/skills/generate-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generate-video", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ArcReel/ArcReel generate-videoInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ArcReel/ArcReel --skill generate-video -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .github/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-video .github/skills/generate-video && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "generate-video" agent skill from https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-video into .github/skills/generate-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generate-video", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ArcReel/ArcReel --skill generate-video -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ArcReel/ArcReel generate-video --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ArcReel/ArcReel.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agent_runtime_profile/.claude/skills/generate-video .opencode/skills/generate-video && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "generate-video" agent skill from https://github.com/ArcReel/ArcReel/tree/main/agent_runtime_profile/.claude/skills/generate-video into .opencode/skills/generate-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generate-video", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
generate-video为分镜或自包含视频单元生成视频。当用户要求生成或重做视频时使用;支持整集、单项与批量自选. An agent skill from ArcReel/ArcReel.
Generate Video is an agent skill from ArcReel/ArcReel. 为分镜或自包含视频单元生成视频。当用户要求生成或重做视频时使用;支持整集、单项与批量自选。
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/veo_prompts.md`).
It sits in Media & Creative, covering AI video generation. It works with Model Context Protocol. The repository describes itself as: AI Agent 驱动的开源可自部署视频工作台:将小说与剧本转为角色、场景、道具资产、分镜、视频和剪映草稿,支持跨镜头一致性、多供应商与费用追踪 | Self-hosted AI video workspace for stories, storyboards and short-form video production. The licence is AGPL-3.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 08ab3b3. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Generate Video loads about 1.3k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 15 tokens; SKILL.md has 301 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found patterns that need a careful read before installing.
unit_id": "E1S01", "version": 2})`,立即生效,无需用户确认、不收费:Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ArcReel/ArcReel at commit 08ab3b3, republished under its AGPL-3.0 licence (© ArcReel). 301 words, ~1,254 tokens.
.claude/skills/generate-video/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.让 MCP 工具读取 project.json,按 generation_mode × content_mode 分派,并校验剧本骨架:
| 生成模式×创作类型 | 应有骨架 | 分派 | 输出目录 |
|---|---|---|---|
reference_video × narration / drama / ad | video_units[] | task_type="reference_video" → execute_reference_video_task | reference_videos/{unit_id}.mp4 |
storyboard × narration | segments[] | task_type="video" → execute_video_task | videos/scene_{segment_id}.mp4 |
storyboard × drama | scenes[] | 同上 | videos/scene_{scene_id}.mp4 |
storyboard × ad | shots[] | 同上 | videos/scene_{shot_id}.mp4 |
骨架失配时停止入队,按项目生成模式重生成剧本。参考生视频直接消费自包含 video_units[],跳过分镜图。
把每个 video_units[] 条目视为一次独立生成调用:
text)构造统一引用语法 prompt。@[名称] 按首次提及顺序解析,无特殊排序;有资产图用资产图,否则用该资产的全部原图。needs_replan 或发声归属问题时停止该视频单元,先修复规划内容。generated_assets.video_clip 明确指向的现行成片;同名孤儿文件不代表该视频单元已完成。让项目配置、剧本模型与视频能力决定比例、时长和参考图上限,不在调用参数中另写一套数值。
使用 MCP 工具入队;本 skill 不提供 Python 或 Shell 生成脚本。
| 操作 | 工具 |
|---|---|
| 整集生成(默认操作) | mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "episode", "episode_id": 1}}) |
| 单分镜 | mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "scene", "ids": ["E1S01"]}}) |
| 批量自选 | mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "selected", "ids": ["E1S01", "E1S05", "E1S10"]}}) |
| 全部待处理 | mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "all"}}) |
把 target.ids 在分镜图生视频解释为分镜 ID,在参考生视频解释为 unit_id。整集生成的 target.episode_id 是剧本所属那一集的集 ID(与文件名 episode_{集 ID}.json 中的数字相同,取计划 target.episode),不是第几集。
在参考生视频传 video_units[].unit_id:
| 操作 | 工具 |
|---|---|
| 重新生成单个视频单元 | mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "scene", "ids": ["E1U2"]}, "force": true}) |
| 重新生成多个视频单元 | mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "selected", "ids": ["E1U2", "E1U3"]}, "force": true}) |
一次调用完成入队并返回 durable batch;按返回的 poll_after_seconds 调用 get_generation_batch,直到 done: true 后再处理结果:
blocked,带 generation_unit_not_found。selected、force: false 重发,已完成项归 skipped。requested / succeeded / failed / blocked 逐 ID 返回,
结构与问题码见 .claude/references/generation-results.md。视频单元的 current 版本决定预览、剪辑与导出用哪一版。挑更好的版本时调用
mcp__arcreel__select_video_version({"unit_id": "E1S01", "version": 2}),立即生效,无需用户确认、不收费:
params.available_versions 重选。视频请求只看剧本:一律按视频单元的编排时长申请档位,准入、报价与恢复都与项目的旁白交付方式无关,
未配置 TTS 的项目照常生成与恢复视频。旁白配音在剪辑阶段单独生成(generate-narration-audio)。
generate_videos 没有 narration_delivery 参数,带上会被拒绝。
视频整批请求是全有或全无:准入 admitted 时整批入队,blocked 或 confirmation_required 时
一个任务都不入队。Web 与 Agent 走同一套准入与同一套请求选择语义,没有 Agent 专属的宽松通道。
参考生视频按视频单元的引用状态选择生效档位,把编排时长投影到模型支持的申请档位。申请档位不同于编排时长时
预检返回 reference_duration_confirmation_required,逐档位向用户说明涉及的视频单元、编排秒数、申请秒数
与变长/变短;确认后经 confirmed_request_durations(按 unit_id 记档位)让原目标集合仍作为一批重发:
mcp__arcreel__generate_videos({"script": "episode_1.json", "target": {"scope": "episode", "episode_id": 1},
"confirmed_request_durations": {"E1U1": 8}})要在提交前先把费用交给用户确认时(如 edit-video 的勾选清单),带 "preview": true 预检:
同一份准入,不入队,返回逐视频单元的预计费用与档位变化。用户确认后正式提交时原样带上预检给出的
confirmed_request_durations,不会再收到档位确认。
被拒时逐视频单元报告 unit_id、problem.code、原因与 problem.action;通过的视频单元带
generation_batch_admission_withheld,其 blocked_unit_ids 指出是被谁挡住的,如实说明这层因果。
不要把整批拆小去先跑通过的那一半——那既绕开全有或全无,也会重复提交已经付过费的视频单元。
能力无法解析时把工具错误作为 blocker,先修复模型能力声明。
task_state(队列任务)、provider_checkpoint(供应商是否已提交)、artifact_status(产物
current / stale / missing / blocked)与 workflow 步骤状态互相独立,分开陈述:「任务成功」不等于
「当前产物有效」。provider_checkpoint.submitted 为真表示供应商侧很可能已计费;任务
interrupted 表示没有供应商裁决,一律按 problem.action 决定;该情形通常交回
wait_for_task(任务可能仍在跑并正常落地),不要自行改成 retry。
stale 产物照常可预览、可导出、可参与成片,服务端会复用、不会自动重生;是否重做由用户明确决定。 不自动删除、覆盖或重生任何已付费产物与历史版本。
generated_assets.video_clip 作为成片归属。让 MCP 工具按生成模式构建 Prompt:
image_prompt、video_prompt 与分镜图。text)与编排时长。novel_text 放入视频 Prompt;旁白由独立音频流程处理。按项目生成模式检查:
needs_replan。@[名称] 的首次提及顺序解析;未登记的提及只产生警告、不阻断入队,让服务端按 max_reference_images 裁剪。reference_videos/{unit_id}.mp4。© ArcReel, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file (references) in agent_runtime_profile/.claude/skills/generate-video of ArcReel/ArcReel.
Open the folder on GitHubat commit 08ab3b3
Generate Video next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Generate Video this skillArcReel/ArcReel | 5.4k | — | ~1.3k | Automated safety check: Warn | AGPL-3.0 | |
| Clipmivo VideoBarneyD66/clipmivo-tools | 142 | — | ~945 | Automated safety check: Pass | MIT | |
| ComfyUI Local DriverSlavaSexton/ComfyUI-Agent-Kit | 105 | — | ~12k | Automated safety check: Pass | Apache-2.0 | |
| Image Edit Workbenchhenjicc/Henji-AI | 254 | — | ~502 | Automated safety check: Pass | Apache-2.0 | |
| Checkffroliva/gflow-cli | 266 | — | ~2.6k | Automated safety check: Pass | MIT | |
| OrchestrationOrkas-AI/Orkas-VideoStudio | 498 | — | ~3.4k | Automated safety check: Pass | MIT |
BarneyD66/clipmivo-tools
Create and manage AI video tasks through ClipmivoAI using its MCP server, CLI or REST API.
SlavaSexton/ComfyUI-Agent-Kit
Drives a local ComfyUI install over its HTTP API to generate and edit images, video and audio, with per-model prompt recipes and workflow guidance.
henjicc/Henji-AI
在痕迹AI修图、调色、抠出或选中主体、移除物体、修补瑕疵、编辑图层与蒙版、导出图片或流转图片产物时使用。视频时间线与成片用 video-edit-workbench;写代码画面用 video-edit-code-creation。
ffroliva/gflow-cli
Auto-fix lint and formatting, then report types and tests. An agent skill from ffroliva/gflow-cli.
Orkas-AI/Orkas-VideoStudio
The master program for producing or editing a video end to end — read this at the START of any video task (after video-router), then follow the gates and the per-line steps.
scenario-labs/skills
A skill your agent uses when generating or editing video with Runway models on Scenario via MCP: text-to-video or image-to-video from a first frame with Gen4.5 (cinematic motion, sequenced actions…
ArcReel/ArcReel
PR AI review 收敛。用户或 team-lead 要求启动或继续审查—修复循环时使用;供本地实现者或受委派的 review-looper 调用,不用于 GitHub reviewer 产出审查意见或仅处理单条评论。
ArcReel/ArcReel
在撰写、改写、校对或审阅中文技术文档、产品文案、界面文案、Markdown 文档、API 说明、操作手册、故障排查或运维文档时使用。采用克制、准确、可扫读的中文技术写作风格;保留原文事实、限制和机器可读内容;按任务读取术语排版、API 状态文案、项目覆盖或受控中文技术写作参考。
ArcReel/ArcReel
Translate every dirty ArcReel documentation source into English and refresh the translation lockfile.
ArcReel/ArcReel
当用户要求把采用 JSON 提交后轮询协议的视频或图片供应商接入 ArcReel,或要求编写、验证、测试、保存自定义调用端点定义时使用。
ArcReel/ArcReel
把一个 Spec 的全部子 issue(或一组显式 issue)组建团队无人值守跑到全部合并或明确暂停. An agent skill from ArcReel/ArcReel.
ArcReel/ArcReel
根据最近的 git 改动,更新面向用户的文档(README 双语、入门教程、部署、剪映导出等)。手动调用. An agent skill from ArcReel/ArcReel.
Works with
Categories
为分镜或自包含视频单元生成视频。当用户要求生成或重做视频时使用;支持整集、单项与批量自选. An agent skill from ArcReel/ArcReel. Generate Video is an agent skill from ArcReel/ArcReel.
Generate Video fits situations like: tasks that involve AI video generation.
Run `npx skills add ArcReel/ArcReel --skill generate-video -a claude-code`. Or copy the skill folder (agent_runtime_profile/.claude/skills/generate-video in ArcReel/ArcReel) into .claude/skills/generate-video in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ArcReel/ArcReel --skill generate-video -a codex`. Or copy the skill folder (agent_runtime_profile/.claude/skills/generate-video in ArcReel/ArcReel) into .agents/skills/generate-video in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ArcReel/ArcReel --skill generate-video -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/generate-video, .gemini/skills/generate-video, .github/skills/generate-video and .opencode/skills/generate-video in your project.
SKILL.md names no scripts, command-line tools or credentials: Generate Video is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.
Generate Video is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.1k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Generate Video: Clipmivo Video (BarneyD66/clipmivo-tools, 142 stars), ComfyUI Local Driver (SlavaSexton/ComfyUI-Agent-Kit, 105 stars), Image Edit Workbench (henjicc/Henji-AI, 254 stars) and Check (ffroliva/gflow-cli, 266 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ArcReel (a GitHub organization) maintains it in ArcReel/ArcReel, which has 5,399 GitHub stars. The repository holds 18 skills in this directory. The repository was last updated on October 9, 2026.
Source: ArcReel/ArcReel on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.